EDGE2: advancing the prioritisation of threatened evolutionary history for conservation action
Bibliographic record
Abstract
Abstract The global biodiversity crisis threatens the natural world and its capacity to provide benefits to humans into the future. The conservation of evolutionary history, captured by the measure phylogenetic diversity (PD), is linked to the maintenance of these benefits and future options. The Evolutionarily Distinct and Globally Endangered (EDGE) metric has, since 2007, been used to identify species for conservation action that embody large amounts of threatened evolutionary history. In 2017, we convened a workshop to update the EDGE metric to incorporate advances in the field of phylogenetically-informed conservation. Building on that workshop, we devised the metric ‘EDGE2’, which we present here. EDGE2 uses a modular, tiered approach to provide priority rankings—and associated measures of uncertainty in both phylogenetic and extinction risk data—for all species in a clade. EDGE2 takes into account the extinction risk of closely-related species to better reflect the contribution a species is expected to make to overall PD in the future. We applied EDGE2 to the world’s mammals to identify an updated list of priority EDGE species and compare the results with the original EDGE approach. Despite similarity in the priority lists produced between EDGE and EDGE2, EDGE2 places greater priority on species with fewer close relatives on the Tree of Life. As we approach a crossroads for global biodiversity policy, EDGE2 exemplifies how academic and applied conservation biologists can collaborate to guide effective priority-setting to conserve the most irreplaceable components of biodiversity upon which humanity depends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".